 
Authors: Kevin P. Murphy (2012)  The book is written in an informal, accessible style, complete with pseudocode for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics.

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Authors: Ameet V Joshi (2020)  This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state.

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Authors: Vũ Hữu Tiệp (2018)  Hướng dẫn các bạn trẻ làm quen các khái niệm, kỹ thuật và thuật toán cơ bản cho các bài toán Học máy (ML). Những khái niệm cơ bản trong ML, xây dựng các mô hình ML, các thuật toán ML phổ biến như mạng neuron nhân tạo, kỹ thuật tối ưu phổ biến cho các bài toán tối ưu không ràng buộc

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Authors: Andriy Burkov (2020)  The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run nonstop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use wellestablished tools and methodologies for doing all of this effectively and efficiently.

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Authors: Hall, Patrick (2023)  This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve realworld AI/ML system outcomes for organizations, consumers, and the public.

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Authors: Michael Beyeler (2017)  This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical realworld tasks.

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Authors: Mark Fenner (2020)  The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an absolute beginner.

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Authors: Xiaowei Huang (2023)  The book aims to improve readers’ awareness of the potential safety issues regarding machine learning models. In addition, it includes uptodate techniques for dealing with these issues, equipping readers with not only technical knowledge but also handson practical skills.

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Authors: Bernhard Mehlig (2023)  This modern and selfcontained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a wellestablished undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the boo...

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Authors: Gallatin, Kyle (2023)  This practical guide provides more than 200 selfcontained recipes to help you solve Machine Learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikitlearn, you'll be able to address specific problems all the way from loading data to training models and leveraging neural networks

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Authors: Maria Schuld (2021)  This book offers an introduction into quantum machine learning research, covering approaches that range from "nearterm" to faulttolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data.

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Authors: Abhijit Ghatak (2017)  This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it's applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning

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Authors: Brett Lantz (2019)  Machine Learning with R, Third Edition provides a handson, readable guide to applying machine learning to realworld problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.

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Authors: Lantz, Brett (2016)  The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with realworld data and realworld problems. This book helps uncover the largescale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to pl...

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Authors: Semwal, Vijay Bhaskar (2023)  The 64 papers presented in this twovolume set were thoroughly reviewed and selected from 399 submissions. The papers are organized according to the following topical sections: machine learning and computational intelligence; data sciences; image processing and computer vision; network and cyber security.

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Authors: Doriya, Rajesh (2023)  This book aims to develop an understanding of image processing, networks, and data modeling by using various machine learning algorithms for a wide range of realworld applications. In addition to providing basic principles of data processing, this book teaches standard models and algorithms for data and image analysis.

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Authors: Böhm, Volker (2017)  This textbook offers a unique approach to macroeconomic theory built on microeconomic foundations of monetary macroeconomics within a unified framework of an intertemporal general equilibrium model extended to a sequential and dynamic analysis. It investigates the implications of expectations and of stationary fiscal policies on allocations, on the quantity of money, and on the dynamic evolution of the economy with and without noise.

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Authors: N. Gregory Mankiw (2019)  Mankiw’s Macroeconomics has been the number one book for the intermediate macro course since the publication of the first edition. It maintains that bestselling status by continually bringing the leading edge of macroeconomics theory, research, and policy to the classroom, explaining complex concepts with exceptional clarity. This new edition is no exception, with Greg Mankiw adding emerging macro topics and frontline empirical research studies, while improving the book's already exemplary focus on teaching students to apply the analytical tools of macroeconomics to current events and policies.

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Authors: Abel, Andrew (2020)  The 10th Edition features new applications, boxes, and problems throughout. It also reflects recent events and developments in the field, such as the recent crisis in the US and Europe and the many new tools used by the Federal Reserve in response.

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Authors: James D. Gwartney (2011)  The new edition reflects current economic conditions, helping students apply economic principles to the world around them. You'll find analysis and explanation of measures of economic activity applied to today's markets and highlighting the recession of 20082009, plus text on the lives and contributions of notable economists. Common economic myths are dispelled, and the "invisible hand" metaphor is applied to economic theory, demonstrating how it works to stimulate the economy.
